Analyzing Meta-Heuristic Algorithms for Task Scheduling in a Fog-Based IoT Application

In recent years, the increasing use of the Internet of Things (IoT) has generated excessive amounts of data. It is difficult to manage and control the volume of data used in cloud computing, and since cloud computing has problems with latency, lack of mobility, and location knowledge, it is not suitable for IoT applications such as healthcare or vehicle systems. To overcome these problems, fog computing (FC) has been used; it consists of a set of fog devices (FDs) with heterogeneous and distributed resources that are located between the user layer and the cloud on the edge of the network. An application in FC is divided into several modules. The allocation of processing elements (PEs) to modules is a scheduling problem. In this paper, some heuristic and meta-heuristic algorithms are analyzed, and a Hyper-Heuristic Scheduling (HHS) algorithm is presented to find the best allocation with respect to low latency and energy consumption. HHS allocates PEs to modules by low-level heuristics in the training and testing phases of the input workflow. Based on simulation results and comparison of HHS with traditional, heuristic, and meta-heuristic algorithms, the proposed method has improvements in energy consumption, total execution cost, latency, and total execution time.

• Publication year

  2022

• Venue

  Algorithms

• Publication date

  2022-10-26

• Fields of study

  Computer Science, Engineering, Environmental Science

• Identifiers
  DOI 10.3390/a15110397
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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